Evidence map›Paper›PMID 40830504›Full record

ReviewBioData mining2025

Skin in the game: a review of computational models of the skin.

Seda Ceylan, Didem Demir, Cayla Harris, Semih Latif İpek, Vasileios Vavourakis, Marco Manca, Sandrine Dubrac, Roman Bauer

Abstract readReview
In one paragraph

Review in BioData mining, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Seda CeylanDepartment of Bioengineering, Adana Alparslan Türkeş Science and Technology University, Adana, Türkiye.
Didem DemirDepartment of Chemistry and Chemical Process Technologies, Tarsus University, Mersin, Türkiye.
Cayla HarrisNICE research group, School of Computer Science and Electronic Engineering, University of Surrey, Guildford, United Kingdom.
Semih Latif İpekDepartment of Food Engineering, Adana Alparslan Türkeş Science and Technology University, Adana, Türkiye.
Vasileios VavourakisDepartment of Medical Physics and Biomedical Engineering, University College London, London, UK.
Marco MancaSCimPulse Foundation, Geleen, The Netherlands.
Sandrine DubracDepartment of Dermatology, Venereology and Allergology, Medical University of Innsbruck, Innsbruck, Austria.
Roman BauerNICE research group, School of Computer Science and Electronic Engineering, University of Surrey, Guildford, United Kingdom. r.bauer@surrey.ac.uk.

Funding

European Cooperation in Science and Technology,Belgium CA21108
6 · The paper itself

Abstract

With the vast advances in computing technology, computational (or in silico) modelling has emerged as a transformative tool in dermatology. These findings can provide novel insights into complex biological processes and aid in the development of innovative therapeutic and regenerative strategies for the skin. Modelling combines experimental data and knowledge across multiple disciplines, serving as a common framework to elucidate the workings of the skin. From a biomedical perspective, the mechanisms of skin diseases can be studied by simulating cellular interactions and signalling pathways. Computational investigations of these mechanisms can be categorised into two distinct approaches: data-driven and model-based. Data-driven approaches allow the diagnosis of skin diseases on the basis of data collection via imaging or feedback from portable sensors, often yielding performance exceeding that of their human counterparts. Model-based methods are well suited to address topics such as skin cell biology and biomechanics, contributing to wound healing and skin cancer research. Furthermore, such modelling has found utility in the development of virtual skin models and skin-on-chip devices, enabling the prediction of skin responses to various substances, including cosmetics and drugs. In the realm of dermatological surgery, computational tools have been instrumental in optimizing surgical planning and improving clinical outcomes. While significant advancements have been made, challenges such as data availability, model validation, and interdisciplinary collaboration persist. This review highlights the current state-of-the-art in computational modeling in dermatology, identifies key challenges, and outlines its prospects.

Indexed as

Computational dermatologyDermisEpidermisIn silicoModellingSkinSkin biomechanics

Identifiers

PMID40830504
PMCPMC12366154

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.